Search Results (124)

Climate change has intensified over recent decades, prompting shifts in forest management strategies, particularly in the Sudetes region of Poland, where native species like Norway spruce (Picea abies), European beech (Fagus sylvatica), and silver fir (Abies alba) have historically dominated. To address these changes, non-native species such as Douglas fir (Pseudotsuga menziesii) have been introduced as potential alternatives. This study, conducted in the Jugów and Świerki forest districts, compared the soil properties and water retention capacities of Douglas fir (Dg) and Norway spruce (Sw) stands (age classes from 8–127 years) in the Fresh Mountain Mixed Forest Site habitat. Field measurements included temperature, humidity, organic matter content, water capacity, and granulometric composition. Results indicate that, in comparison to Sw stands, Dg stands were consistently linked to soils that were naturally finer textured. The observed hydrological changes were mostly supported by these textural differences: In all investigated circumstances, Dg soils demonstrated greater water retention, displaying a water capacity that was around 5% higher. In addition to texture, Dg stands showed reduced soil water repellency and a substantially greater organic matter content (59.74% compared to 27.91% in Sw), which further enhanced soil structure and moisture retention. Conversely, with increasing climatic stress, Sw soils, with coarser textures and less organic matter, showed decreased water retention. The study highlights the importance of species selection in sustainable forest management, especially under climate change. Future research should explore long-term ecological impacts, including effects on microbial communities, nutrient cycling, and biodiversity, to optimize forest resilience and sustainability. Full article

Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models. Full article

Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness. Full article

This study, conducted in Mersin, a Mediterranean sub-tropical area, examined the potential of avocado and pitaya to thrive under current and future climate conditions. Researchers utilized climate and soil data, initially selecting 14 parameters (mean annual temperature, mean minimum temperature of the coldest month, mean maximum temperature of the warmest month, mean annual precipitation, soil texture, soil depth, land use capability, soil pH, soil organic carbon, soil salinity, land cover, elevation, slope, and groundwater level) for analysis, which were narrowed down to 12 after correlation analysis. The potential distributions were projected using the MaxEnt model for current and future scenarios. Three global climate models—HadGEM3-GC31-LL, MPI-ESM1-2-HR, and GFDL-ESM4—were utilized under the SSP2-4.5 and SSP5-8.5 scenarios. Under SSP2-4.5, an average increase of 1.32%, 1.95%, and 4.02% in the “S1” class is expected. For SSP5-8.5, average gains of 1.33%, 1.58%, and 0.77% are projected. In Pitaya, the “S1” class in SSP2-4.5 is expected to increase by 0.96% compared to the first model and decrease by 7.06% and 5.71% compared to the other models, respectively. Under SSP5-8.5, the changes are determined to be 1.49%, −7.27%, and −7.28%, respectively. Our findings indicate that climate change poses a significant threat to the region; however, the application demonstrates that agricultural activities can remain sustainable despite climate change impacts. Full article

Earthworms are important in agriculture in the process of soil fertilization and influence its physicochemical properties. The taxonomic classification of earthworms using morphological characteristics requires experts, is difficult, and can require specimen dissection to extract detailed anatomical studies. Molecular techniques are time-consuming and expensive. The objective of this study was to distinguish earthworms belonging to different genera, Eisenia, Dendrobaena, and Lumbricus, using an innovative approach involving machine learning models built based on image texture parameters from individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S. The earthworms Eisenia fetida, Dendrobaena ssp., and Lumbricus terrestris were used as research materials. Image acquisition was performed using a flatbed scanner on a black background. In the case of each earthworm, 2172 texture parameters from images in individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S were extracted. Textures after selection were used to develop classification models using machine learning algorithms. The earthworms Eisenia fetida, Dendrobaena ssp., and Lumbricus terrestris were distinguished with the accuracy reaching 100% for models built using Logistic, Ensemble, and Narrow Neural Network. All earthworms were correctly classified. Also, in the case of other models, earthworm classes were distinguished with high accuracies, such as 99% (Naive Bayes, Random Forest, SVM, KNN), 97% (Simple Logistic), and 94% (KStar). For the most important species, E. fetida, the correctness of the species identification was confirmed by direct RNA sequencing. The application of image analysis and machine learning turned out to be a non-destructive, inexpensive, and objective approach to distinguishing earthworms belonging to different genera. Full article

The study of the hydrological regimes of rivers in different regions of the globe has revealed the need to include the soil moisture content in flood prediction models. This paper investigates the nature of the dependence of soil moisture content on soil texture in the East Kazakhstan region. Data from ERA-5-land reanalysis, soil maps, hydrogeological maps, and the meteorological data of Kazhydromet were used. The years for analysis were selected due to their different moisture conditions. This study analyzed soil moisture within the root zone (0–28 cm depth). A JavaScript-based algorithm was developed in Google Earth Engine to analyze soil moisture and total precipitation across five Soil Texture Index categories during the growing seasons (April–September) of 2013, 2022, and 2023. Final cartographic processing and spatial distribution analysis were conducted using ESRI ArcGIS Pro 3.3. The study of soil moisture’s relationship with different soil textures in the East Kazakhstan region has revealed several key trends. The maximum values of soil moisture for each texture class change very slightly from year to year. The minimum soil moisture values fluctuate more strongly from year to year. The regression analysis demonstrates a statistically significant relationship between precipitation and soil moisture. The best performance is achieved when using a 1-day lag for 2013 and varying optimal lags for 2022 and 2023 (ranging from 1 to 3 days) during the high-precipitation period (months 6–9), with filtering applied to remove days with negligible rainfall. Full article

Durum wheat (Triticum turgidum subsp. durum), though less widespread than soft wheat, is crucial in Mediterranean countries. Agriculture significantly contributes to global climate change by emitting greenhouse gases, particularly nitrous oxide, which accounts for about 6% of global warming because of its long atmospheric lifetime and heat-trapping capacity. Soil fertility is influenced by the interplay of its physical, chemical, and biological properties, which, in turn, affect the production of nitrous oxide (N₂O), a potent greenhouse gas. The yield-scaled N₂O emission index, which measures N₂O emissions relative to crop yield, is used to develop sustainable agricultural strategies. Our study aimed to compare the effects of organic vs. conventional fertilization on durum wheat yield and N₂O emissions across three soils differing in texture. The study was carried out from autumn 2020 to spring 2021 in Portici (Naples, Italy). A factorial combination was applied, involving three different texture classes (clay, sand, and loam) and four fertilization strategies (no fertilization, compost, digestate, and mineral fertilization). Our results highlight that in sandy soil, wheat yield reached its highest values, particularly under digestate fertilization (+74.5%) and, interestingly, with lower cumulative N₂O emissions (−16%). However, in sandy soil, the protein content of kernels was lower, similar to that recorded for the fertilization with digestate. Full article

Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain reflectometry (TDR) technique is a geophysical method that allows, in a time-varying electric field, the determination of dielectric permittivity and electrical conductivity for a wide class of porous materials. Measuring the volumetric water content in soils is the most frequent application of TDR in soil science and soil hydrology. TDR has grown in popularity over the last 40 years because it is a practical and non-destructive technique that provides laboratory and field-scale measurements. However, a significant limitation of this technique is the relatively high cost of TDR devices, despite the availability of a range of commercial systems with varying prices. This paper aimed to design and implement a low-cost, compact TDR device tailored for classical hydrological applications. A series of laboratory experiments were carried out on soils of different textures to calibrate and validate the proposed measuring system. The results show that the device can be used to obtain predictions for monitoring soil water status with acceptable accuracy (R² = 0.95). Full article

Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was performed considering soil physical, chemical, and microbial data gathered from an array of apple orchards and strawberry plantations managed by organic or conventional methods and located in different European climatic zones. Results: The SOM analysis considering the “climatic zone” categorical variables was able to discriminate the samples from the three zones for both crops. The zones were associated with different soil textures and chemical characteristics, and for both crops, the Continental zone was associated with microbial parameters—including biodiversity indices derived from the NGS data analysis. However, the SOM analysis based on the “management method” categorical variables was not able to discriminate the soils between organic and integrated management. Conclusions: This study allowed for the discrimination of soils of medium- and long-term fruit crops based on their pedo-climatic characteristics and associating these characteristics to some indicators of the soil biome, pointing to the possibility of better understanding the interactions among diverse variables, which could support unraveling the intricate web of relationships that define soil quality. Full article

Soil salinity is a major constraint to soil health and crop productivity, especially in arid and semi-arid regions. The most accurate measurement of soil salinity is considered to be the electrical conductivity of saturated soil extracts (EC_e). Because this method is labor-intensive, it is unsuitable for routine analysis in large soil sampling campaigns. This study aimed to identify the best models to estimate soil salinity based on EC_e in relation to a rapid electrical conductivity (EC) measurement in soil/water (referred to as S:W henceforward) extracts. We evaluated the relationship between EC_e and the EC_S:W extract ratios (1:1, 1:2, and 1:5) in salt-affected soils from the semi-arid Sehb El Masjoune region of Morocco. The soil salinity in this region is 0.5 to 235 dS/m, as determined by the EC_e method. A total of 125 soil samples, from topsoil (0–15 cm) and subsoil (15–30 cm) with mainly fine to medium textures, were analyzed using linear, logarithmic, and second-order polynomial regression models. The models included all samples or grouped samples according to soil texture (fine, medium) or specific textural classes. The mean EC_e values were 2.6, 3.1, and 7.9 times greater than the EC of 1:1, 1:2, and 1:5 S:W extracts, respectively. Polynomial regression models had the best predictive accuracy, R² = 0.98, and the lowest root mean square error of 10.6 to 10.7 dS/m for the EC_S:W extract ratios of 1:5 and 1:2. The polynomial models could represent the non-linear relationships between EC_e and salinity indicators, especially in the 80–170 dS/m salinity range, where other models typically underestimate the salinity. These results confirm that advanced regression techniques are suitable for predicting soil salinity in a salt-affected semi-arid region. The site-specific models outperformed previously published models, because they consider the spatial variability and heterogeneity of the salinity in the study area explicitly. This confirms the importance of calibrating soil salinity models according to the local soil and environmental conditions. Consequently, we can undertake soil salinity assessments in hundreds of samples by using the simple, rapid EC_S:W extraction method as a direct indicator of EC and extrapolate to EC_e with a polynomial regression model. Our approach enables the widespread soil salinity assessments that are needed for land-use planning, irrigation management, and crop selection in salt-affected landscapes. Full article

In the context of contaminated site remediation, the fate of chlorinated solvents in the subsurface and subsequent groundwater contamination is influenced by soil properties governing sorption. The solid–water distribution coefficient (K_d) is a key parameter for modeling contaminant distribution and transport, essential for risk assessment and remediation planning. This study evaluated tetrachloroethene sorption isotherms in 34 low-organic-carbon soils from the Czech Republic, assessing the influence of soil properties on K_d. Soil samples exhibited variability in organic carbon content (˂0.05–0.81%), with clay ranging from 0% to 64.9%, silt 5.1% to 71.2%, and sand 5.2% to 88.9%, specific surface area (0.41–64.39 m² g⁻¹), particle density (2.05–4.09 g cm⁻³), and porosity (43.5–67.3%). Batch experiments were conducted using standard procedures, with K_d values ranging from 0.379 to 2.272 L kg⁻¹. Statistical analysis grouped the soils into three textural classes: sandy, clayey fine, and silty loam. The findings reveal that organic carbon content and specific surface area are the primary predictors of K_d, while clay and sand also play a significant role in shaping sorption behavior. Multivariate regression models explained 63.6% to 98.5% of K_d variability with high accuracy, as indicated by low root means square error (0.070–0.329) and mean absolute percentage error (3.8–28.8%) values. These models offer reliable predictions of sorption behavior, providing valuable tools for risk assessment and remediation strategies. Full article

The systematic identification of appropriate sites for different rainwater harvesting (RWH) structures may contribute to better success of crop production in such areas. One approach to improving crop yields in North Kordofan, Sudan, that is mostly adaptable to the changing climate is in-field water harvesting. The main objective of this study is to employ a geographical information system (GIS) in order to identify the most suitable sites for setting in situ water harvesting structures, aiming to address climate change in this area. A GIS-based model was developed to generate suitability maps for in situ RWH using multi-criteria evaluation. Five suitability criteria (soil texture, runoff depth, rainfall surplus, land cover, and slope) were identified; then, five suitability levels were set for each criterion (excellent, good, moderate, poor, and unsuitable). Weights were assigned to the criteria based on their relative importance for RWH using the analytical hierarchy process (AHP). Using QGIS 2.6.1 and ArcGIS 10.2.2 software, all criterion maps and suitability maps were prepared. The obtained suitability map for the entire region showed that 40% of the region area fell within the “good” class, representing 7419.18 km², whereas 26% of the area was “excellent”, occupying 4863.75 km². However, only 8.9% and 15.6% of the entire region’s area were “poor” and “unsuitable” for RWH, respectively. The suitability map of the delineated pilot areas selected according to the attained FAO data revealed that one location, Wad_Albaga, was found to be in an excellent position, covering an area of 787.811 km², which represents 42.94% of the total area. In contrast, the Algabal location had 6.4% of its area classified as poor and the remaining portion classified as excellent. According to the findings from the validated trial, Wad_Albaga is located in a good site covering 844 km², representing 46.04%, while Algabal is classified as a moderate site, covering 341 km² or 18.6% of the area. This study concluded that the validation of the existing trial closely matched the suitability map derived using FAO data. However, ground data from field experiments provided more accurate results compared to the FAO suitability map. This study also concluded that using GIS is a time-saving and effective tool for identifying suitable sites and discovering the most appropriate locations for rainwater harvesting (RWH). Full article

Agricultural soil maps (ASMs) showing the agricultural land of Poland were prepared at a 1:5000 scale in the 1960s and 1970s. These maps show land suitability groups, soil type, and soil texture (ST) to a depth of 150 cm. Nowadays, these maps are being digitalized and might be a basis for the preparation of modern soil maps at the local, regional, national, and international levels. The agreement between the ST of the topsoil derived from ASMs and the recently evaluated one for eleven fields located in three voivodeships (regions) of Poland was studied. This study considered the examination of soil profiles or augerings and the laboratory analysis of the ST. The agreement between the ST status in the field and that according to the ASMs was field-specific. A complete agreement (purity) within the field was assessed for 5–79% of ST classes and for 23–100% of agronomic categories (ACs), i.e., groupings of similar ST classes. However, the averaged agreement, which treated adjacent ST classes as having a partial agreement, varied from 37 to 88% for ST classes and from 61 to 100% for the ACs among studied fields. These results indicate the variable quality of the information shown on ASMs and the necessity of improving these maps. Full article

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO₃ levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day⁻¹, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season⁻¹. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide. Full article

Due to the multiple pressures from human activities, many freshwater ecosystems are facing degradation. To address this issue, a new approach for assessing stream water quality and ecological (WQE) risk using a multi-criteria analysis through a GIS-based policy tool has been developed. The suggested methodology integrates eight different factors along the contaminant pathway from source to streams, including: (a) rainfall variability, (b) soil texture, (c) soil erodibility, (d) slope, (e) river buffer zone, (f) point source contamination buffer zone, (g) non-point source contamination of NO₃, and (h) non-point source contamination of PO₄. Utilizing fuzzy GIS tools, the above factors and their related maps were spatially overlaid (raster-based suitability for raster reclassification) to obtain the final stream WQE risk map. The final map depicts the spatial distribution of streams concerning their water quality risk and is represented by two classes of WQE risk. The first class is characterized as “appropriate”, in which there is no need for any further actions, while the other one is characterized as “non-appropriate”, indicating that actions should be taken to ensure the sustainability of streams’ water quality. The proposed approach was implemented for the island of Crete, which is located in the Southeast Mediterranean region. The developed methodology was validated using the Hellenic evaluation system (HESY2), an especially established and adapted to the Mediterranean river systems ecological quality metric method, obtained by in situ measurements that were conducted during different monitoring programs (1989–2015). Moreover, this study summarizes appropriate measures and practices that ensure the sustainable management of Mediterranean river basins. These practices can be adopted by local authorities, owners of polluting units, and farmers/breeders to improve the resiliency of streams’ water quality issues in the Mediterranean region. Full article